This document discusses social data intelligence and the challenges of integrating social data across organizations. It defines social data intelligence as insight derived from social data that can be used confidently and at scale alongside other data sources to make strategic decisions. The challenges of integrating social data include dealing with multiple internal interests, requiring new analytical approaches, and social data initially lacking credibility. The document provides an example of how Symantec harvests social data and routes it to the appropriate business functions. It discusses the results Symantec has seen across marketing, customer support, engineering, and other areas. The document also presents a maturity model for social data and trends organizations should consider when using social data.
This document provides a 3-step guide for ensuring data can be trusted to make confident business decisions:
1. Know where data comes from and whether it can be trusted by understanding its source and history of manipulation.
2. Create a unified view of data so everyone accesses the same consistent information using common definitions.
3. Empower all users to access and analyze data through balanced governance that streamlines processes while maintaining oversight.
The document discusses key topics from IBM's Business Analytics Summit in Toronto in 2013. It outlines the four dimensions of big data: volume, velocity, variety, and veracity. It also discusses challenges organizations face in managing big data and key shifts driving the need for smarter analytics. Additionally, it provides examples of how leading organizations are using analytics to gain insights from data and outperform competitors. Finally, it briefly describes several IBM products for big data analytics.
Making advanced analytics work for youYogesh Kumar
Big data and analytics is becoming increasingly important for corporations. Many top companies like Google and Amazon use business models fueled by big data to gain a competitive advantage. Research also shows companies that effectively use big data have productivity and profitability rates 5-6% higher than peers. However, some leaders are still cautious about using big data, due to inability to fully utilize existing analytics programs or prior experiences not meeting expectations. To successfully use big data, organizations need three key capabilities: collecting and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models can improve decision making.
Big data refers to large datasets that are difficult to process using traditional tools. It is important for analyzing large amounts of data to gain insights and make better marketing decisions. Big data can help monitor social media sentiment around brands and forecast trends. While over half of people are unfamiliar with big data, most marketing leaders believe customer data is important for decisions. Big data is increasingly being used to measure corporate social responsibility by gathering stakeholder inputs and identifying external trends. Companies are using big data for social good through community outreach and contributory databases.
Big data and analytics have become increasingly important in the corporate world. Venture capital investments in big data are growing exponentially. Research from MIT shows analytics can increase productivity by 5-6%. However, many companies are unsure how to implement analytics effectively. The document outlines three key capabilities for exploring big data: identify the right data sources, build advanced analytics models, and transform the organization to make better decisions based on data and models. Mastering these skills can help companies gain a competitive advantage.
The document discusses how "big data" is not necessary for effective fraud prevention and risk management. Small, targeted data sets focused on specific customer interactions can provide high-quality signals with low noise. Domain experts who understand customer behaviors and can analyze anomalies are more important than large data hoards or technical specialists. These experts can close performance gaps through root cause analysis, validation, and being given tools to efficiently access and tag relevant information.
This document provides a 3-step guide for ensuring data can be trusted to make confident business decisions:
1. Know where data comes from and whether it can be trusted by understanding its source and history of manipulation.
2. Create a unified view of data so everyone accesses the same consistent information using common definitions.
3. Empower all users to access and analyze data through balanced governance that streamlines processes while maintaining oversight.
The document discusses key topics from IBM's Business Analytics Summit in Toronto in 2013. It outlines the four dimensions of big data: volume, velocity, variety, and veracity. It also discusses challenges organizations face in managing big data and key shifts driving the need for smarter analytics. Additionally, it provides examples of how leading organizations are using analytics to gain insights from data and outperform competitors. Finally, it briefly describes several IBM products for big data analytics.
Making advanced analytics work for youYogesh Kumar
Big data and analytics is becoming increasingly important for corporations. Many top companies like Google and Amazon use business models fueled by big data to gain a competitive advantage. Research also shows companies that effectively use big data have productivity and profitability rates 5-6% higher than peers. However, some leaders are still cautious about using big data, due to inability to fully utilize existing analytics programs or prior experiences not meeting expectations. To successfully use big data, organizations need three key capabilities: collecting and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models can improve decision making.
Big data refers to large datasets that are difficult to process using traditional tools. It is important for analyzing large amounts of data to gain insights and make better marketing decisions. Big data can help monitor social media sentiment around brands and forecast trends. While over half of people are unfamiliar with big data, most marketing leaders believe customer data is important for decisions. Big data is increasingly being used to measure corporate social responsibility by gathering stakeholder inputs and identifying external trends. Companies are using big data for social good through community outreach and contributory databases.
Big data and analytics have become increasingly important in the corporate world. Venture capital investments in big data are growing exponentially. Research from MIT shows analytics can increase productivity by 5-6%. However, many companies are unsure how to implement analytics effectively. The document outlines three key capabilities for exploring big data: identify the right data sources, build advanced analytics models, and transform the organization to make better decisions based on data and models. Mastering these skills can help companies gain a competitive advantage.
The document discusses how "big data" is not necessary for effective fraud prevention and risk management. Small, targeted data sets focused on specific customer interactions can provide high-quality signals with low noise. Domain experts who understand customer behaviors and can analyze anomalies are more important than large data hoards or technical specialists. These experts can close performance gaps through root cause analysis, validation, and being given tools to efficiently access and tag relevant information.
Big Data: Expectations, Obstacles, and The Road to Greater ValueSAP Technology
What can Big Data do for you? A recent report by The Economist Intelligence
Unit sponsored by SAP Services explores these issues and more. Learn more at www.sap.com/bigdata
SparkScore (The Social Net Promoter Score): A methodology for measuring socia...SocialMedia.org
In her Brands-Only Summit presentation, Satmetrix's VP of Innovation and Strategy, discusses SparkScore -- the Social Net Promoter Score.
She explains how this social media analytic marries the insight from structured, survey-based solutions to the "social universe" through a single, standard metric.
This document discusses the risks and benefits of social media for businesses. It notes that the biggest risk is not having a social media strategy, and that a helter-skelter approach will not ensure success. Some benefits include gaining prospective customers, resolving customer issues, improving customer satisfaction and retention rates. The document proposes a social media strategy to align activities with business objectives and help pull the business to success.
Data driven in social meet up 20 minutesAman Sandhu
1) The document discusses trends in big data and data-driven decision making. It notes that by 2017, 30% of enterprise access to big data will be through data broker services and by 2018 there will be a 35% shortfall of data-savvy workers. Using big data fully could increase retailer operating margins by 60%.
2) Common challenges for companies include integrating data from different sources, performing advanced analytics beyond basic reporting, and democratizing data access across all employee levels.
3) The document promotes a company called Keboola that helps connect different data sources to break down data silos and reduce the cost of infrastructure, while also providing ready-to-use advanced analytics applications.
The challenges of big data, how data capable is your business? DQM Group Internet World
The document discusses the challenges of big data and how to measure an organization's data capabilities. It outlines the opportunities big data provides, such as recalculating risk portfolios and analyzing social media data. However, big data also presents challenges like needing a defined data strategy and ensuring talent and technology are aligned. It introduces the data maturity curve to assess an organization's data management practices and provides examples of metrics to measure people, processes, and technology. The document emphasizes having a data strategy, considering third parties and future legislation, and thinking carefully about current capabilities before pursuing big data initiatives.
Big data has received a lot of hype but its potential for improving predictions of human behavior is limited. While marginal gains can be made, major breakthroughs are unlikely because human behavior remains inconsistent, impulsive, and dynamic. The biggest impacts of big data will be in creating new areas and applications, such as location-based services, healthcare, and artificial intelligence. Managers should recognize that data cannot fully predict changing human behavior and ensure their teams use analytics appropriately.
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
This document discusses the limitations of people analytics and big data in assessing success and performance. Specifically, it notes that readily available metrics like quarterly targets may not be the most important factors and that the assumptions built into personality tests can discriminate unfairly. The strongest predictor of future behavior is actually past performance, best evaluated by people familiar with an individual, so big data is not necessarily the best judge of performance.
This document summarizes key findings from a study of nearly 800 marketers at Fortune 1000 companies. The study found that most marketers rely too heavily on intuition and past experiences rather than data when making customer-related decisions. Specifically, over half of the information used comes from gut feelings and previous experiences. While marketers consult data sources, they only depend on data for 11% of decisions. Additionally, almost half of marketers scored poorly on statistical aptitude questions. Though a small group uses data frequently, these "Connectors" tend to overadjust to every change in data and lose sight of overall goals. Relying too heavily on outdated assumptions without updating understandings of shifting consumer behaviors can be problematic. Good marketers are
This document discusses how small businesses can benefit from analyzing big data. It defines big data as large volumes of data from various sources that are created quickly. While big data was once only for large companies, small businesses already have customer data from their website, social media, emails, and CRM that can be analyzed. The document provides examples of how small businesses can use big data for social listening, customer service, and trends/forecasting. It then offers advice on getting started with big data solutions, including using CRM software and analytics tools, and introduces Tabor Consulting as a provider that can help small businesses with big data needs.
State of the Industry: Creating a 360 View: Leveraging the Right Data for Tod...Digiday
Marketers today are striving to combine all forms of consumer information, from basic demographics to purchase behavior and even social and mobile activity, into a single, unified view of their customers. A comprehensive understanding of your customer across channels is the key to developing analytics that can better tailor approaches toward, and thus predict the behavior of, consumers, both online and offline. The quest to identify a single view is nowhere near complete, and while various tactics are adopted to suit the needs of marketers, there is a growing industry debate regarding the ethicality of these practices, especially when the methods of gathering consumer information are either unorthodox or involuntary.
Neustar conducted a study by surveying hundreds of marketers to hear about current practices marketers are implementing in attempt to gain a single view of their customer and how they are currently leveraging this data to efficiently reach their audiences across device and channel. In this session, Paul McConville, Vice President at Neustar will will discuss the effectiveness of optimizing your marketing efforts, the importance of data quality, identity linkages, data access and how the wider legal and moral narrative is shaping norms and realities.
This document contains information about several companies and the work they do with data. It discusses Tala, a company that collects mobile Android data like SMS, payments, and utilities to classify and model user data for credit decisioning. Their goal is to provide financial access to underserved people globally using alternative mobile data instead of traditional credit checks. The document also briefly mentions the work of Brown University, DRI/McGraw-Hill, OneSource Information Services, Compete/WPP, and Dstillery/EveryScreen Media which involves tasks like data cleansing, modeling, forecasting, and measuring online behaviors.
When sales leaders have real-time data at their fingertips, they’ll always be more efficient and successful. Whether it’s identifying opportunities, understanding who top performers are, validating forecasts, highlighting neglected opportunities, or developing talent, better and faster access to real-time data generates more revenue.
But running a data-driven sales org is not an easy task.
Domo polled more than 400 sales leaders and managers across a range of industries to understand their relationship with data. 73 percent of survey respondents said they would consume data more if they could see it one place, and 66 percent said they would consume data more if they could see it in real time.
The problem is, sales leaders often can’t access the data they need. It’s a company’s responsibility to turn data-hungry sales pros into data-driven selling machines. Every minute a sales rep spends creating or analyzing reports takes time away from prospects and customers. Non sales activities are killers in terms of nailing your number.
The document reported statistics from a 2017 global data management benchmark report. Some key findings included: 27% of organizations believed their current customer and prospect data was inaccurate; 57% of U.S. businesses said they maintain high-quality contact records to increase efficiency; and 82% of organizations saw some improvement in revenue growth from improving their data quality solutions. The document contained over 30 statistics examining organizations' data management practices and challenges.
Session on the value of social data and how companies are going "beyond the basics" in using social data for research. Presented at #1amconf (www.http://www.altmetricsconference.com) on
4 Steps to Creating an Effective Sales DashboardDomo
Sales executives deal with a daily barrage of data - forecast numbers, pipeline velocity, lead volume, territory effectiveness, win/loss reports. The biggest challenge is figuring out how to consume the data and translate it into better decision-making. How is this accomplished? The answer for an increasing number of successful sales leaders is an effective sales dashboard.
Discussion Topics include:
· The Explosion of Sales and Marketing Data
· Key Metrics Sales Leaders are Tracking
· Lessons Learned from a Sales Veteran
· New Sales Dashboard Technologies
An analysis of Making Advanced Analytics Work for You by Dominic Barton and D...Tanya Gupta
This document discusses how companies can make advanced analytics work for them. It outlines that fully exploiting data and analytics requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models yield better decisions. Two key features are having a clear strategy for how to use data/analytics to compete and deploying the right technology. The document provides three areas of action for organizational change: developing business-relevant analytics, embedding analytics into front-line tools, and developing capabilities to exploit big data. Managers need to understand how information can be used for key decisions and get creative about potential external/new data sources through targeted efforts.
Big Data: Expectations, Obstacles, and The Road to Greater ValueSAP Technology
What can Big Data do for you? A recent report by The Economist Intelligence
Unit sponsored by SAP Services explores these issues and more. Learn more at www.sap.com/bigdata
SparkScore (The Social Net Promoter Score): A methodology for measuring socia...SocialMedia.org
In her Brands-Only Summit presentation, Satmetrix's VP of Innovation and Strategy, discusses SparkScore -- the Social Net Promoter Score.
She explains how this social media analytic marries the insight from structured, survey-based solutions to the "social universe" through a single, standard metric.
This document discusses the risks and benefits of social media for businesses. It notes that the biggest risk is not having a social media strategy, and that a helter-skelter approach will not ensure success. Some benefits include gaining prospective customers, resolving customer issues, improving customer satisfaction and retention rates. The document proposes a social media strategy to align activities with business objectives and help pull the business to success.
Data driven in social meet up 20 minutesAman Sandhu
1) The document discusses trends in big data and data-driven decision making. It notes that by 2017, 30% of enterprise access to big data will be through data broker services and by 2018 there will be a 35% shortfall of data-savvy workers. Using big data fully could increase retailer operating margins by 60%.
2) Common challenges for companies include integrating data from different sources, performing advanced analytics beyond basic reporting, and democratizing data access across all employee levels.
3) The document promotes a company called Keboola that helps connect different data sources to break down data silos and reduce the cost of infrastructure, while also providing ready-to-use advanced analytics applications.
The challenges of big data, how data capable is your business? DQM Group Internet World
The document discusses the challenges of big data and how to measure an organization's data capabilities. It outlines the opportunities big data provides, such as recalculating risk portfolios and analyzing social media data. However, big data also presents challenges like needing a defined data strategy and ensuring talent and technology are aligned. It introduces the data maturity curve to assess an organization's data management practices and provides examples of metrics to measure people, processes, and technology. The document emphasizes having a data strategy, considering third parties and future legislation, and thinking carefully about current capabilities before pursuing big data initiatives.
Big data has received a lot of hype but its potential for improving predictions of human behavior is limited. While marginal gains can be made, major breakthroughs are unlikely because human behavior remains inconsistent, impulsive, and dynamic. The biggest impacts of big data will be in creating new areas and applications, such as location-based services, healthcare, and artificial intelligence. Managers should recognize that data cannot fully predict changing human behavior and ensure their teams use analytics appropriately.
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
This document discusses the limitations of people analytics and big data in assessing success and performance. Specifically, it notes that readily available metrics like quarterly targets may not be the most important factors and that the assumptions built into personality tests can discriminate unfairly. The strongest predictor of future behavior is actually past performance, best evaluated by people familiar with an individual, so big data is not necessarily the best judge of performance.
This document summarizes key findings from a study of nearly 800 marketers at Fortune 1000 companies. The study found that most marketers rely too heavily on intuition and past experiences rather than data when making customer-related decisions. Specifically, over half of the information used comes from gut feelings and previous experiences. While marketers consult data sources, they only depend on data for 11% of decisions. Additionally, almost half of marketers scored poorly on statistical aptitude questions. Though a small group uses data frequently, these "Connectors" tend to overadjust to every change in data and lose sight of overall goals. Relying too heavily on outdated assumptions without updating understandings of shifting consumer behaviors can be problematic. Good marketers are
This document discusses how small businesses can benefit from analyzing big data. It defines big data as large volumes of data from various sources that are created quickly. While big data was once only for large companies, small businesses already have customer data from their website, social media, emails, and CRM that can be analyzed. The document provides examples of how small businesses can use big data for social listening, customer service, and trends/forecasting. It then offers advice on getting started with big data solutions, including using CRM software and analytics tools, and introduces Tabor Consulting as a provider that can help small businesses with big data needs.
State of the Industry: Creating a 360 View: Leveraging the Right Data for Tod...Digiday
Marketers today are striving to combine all forms of consumer information, from basic demographics to purchase behavior and even social and mobile activity, into a single, unified view of their customers. A comprehensive understanding of your customer across channels is the key to developing analytics that can better tailor approaches toward, and thus predict the behavior of, consumers, both online and offline. The quest to identify a single view is nowhere near complete, and while various tactics are adopted to suit the needs of marketers, there is a growing industry debate regarding the ethicality of these practices, especially when the methods of gathering consumer information are either unorthodox or involuntary.
Neustar conducted a study by surveying hundreds of marketers to hear about current practices marketers are implementing in attempt to gain a single view of their customer and how they are currently leveraging this data to efficiently reach their audiences across device and channel. In this session, Paul McConville, Vice President at Neustar will will discuss the effectiveness of optimizing your marketing efforts, the importance of data quality, identity linkages, data access and how the wider legal and moral narrative is shaping norms and realities.
This document contains information about several companies and the work they do with data. It discusses Tala, a company that collects mobile Android data like SMS, payments, and utilities to classify and model user data for credit decisioning. Their goal is to provide financial access to underserved people globally using alternative mobile data instead of traditional credit checks. The document also briefly mentions the work of Brown University, DRI/McGraw-Hill, OneSource Information Services, Compete/WPP, and Dstillery/EveryScreen Media which involves tasks like data cleansing, modeling, forecasting, and measuring online behaviors.
When sales leaders have real-time data at their fingertips, they’ll always be more efficient and successful. Whether it’s identifying opportunities, understanding who top performers are, validating forecasts, highlighting neglected opportunities, or developing talent, better and faster access to real-time data generates more revenue.
But running a data-driven sales org is not an easy task.
Domo polled more than 400 sales leaders and managers across a range of industries to understand their relationship with data. 73 percent of survey respondents said they would consume data more if they could see it one place, and 66 percent said they would consume data more if they could see it in real time.
The problem is, sales leaders often can’t access the data they need. It’s a company’s responsibility to turn data-hungry sales pros into data-driven selling machines. Every minute a sales rep spends creating or analyzing reports takes time away from prospects and customers. Non sales activities are killers in terms of nailing your number.
The document reported statistics from a 2017 global data management benchmark report. Some key findings included: 27% of organizations believed their current customer and prospect data was inaccurate; 57% of U.S. businesses said they maintain high-quality contact records to increase efficiency; and 82% of organizations saw some improvement in revenue growth from improving their data quality solutions. The document contained over 30 statistics examining organizations' data management practices and challenges.
Session on the value of social data and how companies are going "beyond the basics" in using social data for research. Presented at #1amconf (www.http://www.altmetricsconference.com) on
4 Steps to Creating an Effective Sales DashboardDomo
Sales executives deal with a daily barrage of data - forecast numbers, pipeline velocity, lead volume, territory effectiveness, win/loss reports. The biggest challenge is figuring out how to consume the data and translate it into better decision-making. How is this accomplished? The answer for an increasing number of successful sales leaders is an effective sales dashboard.
Discussion Topics include:
· The Explosion of Sales and Marketing Data
· Key Metrics Sales Leaders are Tracking
· Lessons Learned from a Sales Veteran
· New Sales Dashboard Technologies
An analysis of Making Advanced Analytics Work for You by Dominic Barton and D...Tanya Gupta
This document discusses how companies can make advanced analytics work for them. It outlines that fully exploiting data and analytics requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models yield better decisions. Two key features are having a clear strategy for how to use data/analytics to compete and deploying the right technology. The document provides three areas of action for organizational change: developing business-relevant analytics, embedding analytics into front-line tools, and developing capabilities to exploit big data. Managers need to understand how information can be used for key decisions and get creative about potential external/new data sources through targeted efforts.
Bolsterstone Community Group AGM - Chair's report slidesbolsterstone
This document summarizes the activities of the Bolsterstone Community Group from October 2011 to September 2012. It describes several community events organized by the group, including bulb planting, a Christmas fayre, wreath making, litter picking, a plant sale, Queen's Jubilee celebrations, and a school reunion. It also lists grants received by the group totaling over £77,000 to fund projects like a community garden, heritage projects, and village improvements. Upcoming events are announced, and committee members and partners thanked for their contributions.
The document advertises houses for sale in General Trias, Cavite. The houses have no issues with flooding or fault lines. A 15% down payment is required, with the option to pay over 18 months. Financing is available through in-house or bank loans. The document provides contact information for inquiries.
Slides by Craig McGill (@craigmcgill) from the recent Edinburgh Chamber of Commerce Inspiring Edinburgh event.
If you've any questions, feel free to reach out to Craig via Twitter.
The document discusses predictions and recommendations for social media strategies and digital marketing in 2014. Some of the key points highlighted include: more companies acting as media firms by using various content formats and platforms; a continued shift towards mobile and second screen engagement; less reliance on text-based content and more visual/video content; and getting away from the outdated mantra of "content is king" by focusing more on helpful, engaging content over self-promotional content. The document also emphasizes consistency, responsiveness, storytelling and establishing goals in order to improve engagement and return on investment.
Este documento describe un torno de control numérico (CNC). Un torno CNC utiliza un software de computadora para controlar los ejes X, Y y Z y mecanizar piezas de revolución con precisión y en cantidades. Puede realizar diferentes tipos de mecanizado y su rentabilidad depende del tipo y cantidad de piezas a mecanizar.
Este documento describe varias anomalías del desarrollo intestinal como atresia, divertículo de Meckel y onfalocele. También describe enfermedades como la enfermedad de Hirschsprung, que causa megacolon congénito debido a la falta de células ganglionares. Otras condiciones descritas incluyen isquemia intestinal, hemorroides, diverticulosis, obstrucción intestinal, enterocolitis infecciosa y síndromes de mala absorción. Finalmente, se describen las enfermedades inflamatorias intestinales como la enfermed
El documento proporciona instrucciones para crear una cuenta de Gmail individual y unirse al grupo de Informática Médica II 2010 en Google Groups, ingresando la dirección URL provista. Los estudiantes deben usar el formato de su apellido paterno, apellido materno y nombres como su nick para unirse al grupo.
Este documento describe los elementos fundamentales del fenómeno comunicativo, incluyendo el emisor, receptor, mensaje, código, canal y retroalimentación. Explica que la comunicación implica la transmisión de información de un ente a otro a través de estos elementos. Define cada uno de los componentes clave del proceso comunicativo y concluye que aunque la comunicación ha cambiado con la tecnología, sus elementos básicos siguen siendo los mismos.
El aprendizaje colaborativo es un método de aprendizaje en el que los estudiantes trabajan juntos en grupos para lograr una meta común, intercambiando ideas y críticas. Este método permite que los estudiantes aprendan a través de la discusión y permite ganar experiencia trabajando en equipo. El aprendizaje colaborativo ofrece varias ventajas como ayudar a los estudiantes a incorporarse a un grupo, permitir que los tímidos se sociabilicen mejor, y trabajar juntos para lograr objetivos cualit
Este documento presenta las normas básicas de etiqueta en internet, también conocidas como netiqueta. Explica que a diferencia de la comunicación en persona, en internet no hay señales no verbales, por lo que es importante seguir ciertas reglas para transmitir mensajes de manera efectiva y respetuosa. Luego enumera algunas de estas reglas clave como tratar a los demás con respeto, no usar mayúsculas para no parecer agresivo, y revisar los mensajes antes de enviarlos.
The average enterprise-class company owns 178 social accounts, while 13 departments — including marketing, human resources, field sales, and legal — are actively engaged in social media. Yet social data are still largely isolated from business-critical enterprise data collected from Customer Relationship Management (CRM), Business Intelligence (BI), market research, and other sources. In this report, industry analyst Susan Etlinger demonstrates how leading organizations are deriving actionable intelligence from a holistic view of social and enterprise data, the challenges and opportunities in doing so, and the criteria required to achieve social intelligence maturity.
Social Data Intelligence: Webinar with Susan EtlingerSusan Etlinger
This webinar covers the findings from the Altimeter Group report, Social Data Intelligence, which lays out the imperative for organizations to integrate social data with other data streams in the enterprise. Includes best practices and frameworks, as well as a maturity map to enable organizations to make the best and most strategic use of social data.
Social data intelligence, presented by Susan EtlingerSocialMedia.org
In her Brands-Only Summit presentation, Altimeter Group's Industry Analyst, Susan Etlinger, talks about how leading organizations are deriving actionable intelligence from a holistic view of social enterprise data.
She discusses the challenges, opportunities, and the criteria required to achieve social intelligence maturity.
To implement data-centric security, while simultaneously empowering your business to compete and win in today’s nano-second world, you need to understand your data flows and your business needs from your data. Begin by answering some important questions:
•
What does your organization need from your data in order to extract the maximum business value and gain a competitive advantage?
•
What opportunities might be leveraged by improving the security posture of the data?
•
What risks exist based upon your current security posture? What would the impact of a data breach be on the organization? Be specific!
•
Have you clearly defined which data (both structured and unstructured) residing across your extended enterprise is most important to your business? Where is it?
•
What people, processes and technology are currently employed to protect your business sensitive information?
•
Who in your organization requires access to data and for what specific purposes?
•
What time constraints exist upon the organization that might affect the technical infrastructure?
•
What must you do to comply with the myriad government and industry regulations relevant to your business?
Finally, ask yourself what a successful data-centric protection program should look like in your organization. What’s most appropriate for your organization?
The answers to these and other related questions would provide you with a clearer picture of your enterprise’s “data attack surface,” which in turn will provide you with a well-documented risk profile. By answering these questions and thinking holistically about where your data is, how it’s being used and by whom, you’ll be well positioned to design and implement a robust, business-enabling data-centric protection plan that is tailored to the unique requirements of your organization.
This document summarizes a presentation about using data-driven marketing approaches. It discusses trends like treating customers like royalty through personalized experiences, using big data and predictive analytics to gain insights about customers. It also covers challenges of data silos and lack of contextual data. The presentation advocates for using multi-dimensional customer data management, predictive analytics, streaming analytics and bi-directional digital platforms to better understand and interact with customers in real-time.
Intel, Cloudera and guest speaker Forrester Research, Inc. discuss the strategy of pervasive analytics and real life examples of how analytics have already been embedded into applications and workflows.
Trust: How to Get It, Keep It, Measure It and Regain It Paine Publishing
This document summarizes Katie Paine's presentation on trust measurement. It discusses what trust is, factors that influence trust like competence and integrity, and how trust can be measured. It provides examples of trust measurement statements and describes a case study where a non-profit used a trust index to measure different trust drivers among stakeholders. Key takeaways are to define important stakeholders, identify relevant trust questions, conduct surveys over time, and analyze results for insights to improve trust.
IBM's InfoSphere software helps organizations successfully leverage big data by providing an understanding of their data. It addresses the challenges of big data's four V's (volume, variety, velocity, and veracity) by automating data integration and governance. This helps boost confidence in big data by establishing standard terminology, tracing data lineage, and separating useful "good" data from unnecessary "bad" data. As a result, organizations can more accurately analyze big data and act on the insights with confidence.
The document discusses big data analytics and provides tips for organizations looking to implement big data initiatives. It notes that while organizations have large amounts of customer, sales, and other operational data, most are not effectively analyzing and extracting insights from this data. The value is in using analytics to uncover hidden patterns and correlations to help businesses make better decisions. However, most companies currently take a slow, manual approach to data compilation and analysis. The document recommends that organizations consider big data as a business solution rather than just an IT problem. It suggests taking a journey approach, focusing on insights over data, using proven analytics tools, and delivering early business value from big data projects in order to justify further investment.
Presentation big data and social media final_videoramikaurraminder
The document discusses the challenges and opportunities of analyzing big data from social media. It notes that social media generates the largest record of human activity but making sense of the unstructured data is a challenge. It provides examples of how companies use social media data for applications like credit risk assessment and personalized recommendations. The document also discusses privacy and ethical issues with social media data mining, and best practices for social media marketers to leverage big data insights.
Big data refers to large, complex datasets that are difficult to process using traditional methods. It is growing exponentially from sources like the internet, sensors, and social media. Big data has characteristics like volume, velocity, variety, and veracity. While it enables better decision making and customer insights, big data also poses challenges around privacy, security, complexity, and cost. Effective use of big data requires investment in tools, skills, and governance strategies.
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Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
Managing it security and data privacy securityAlpesh Doshi
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My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals
This document discusses data ethics and provides 5 key principles of data ethics for business professionals:
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2) Transparency - individuals have a right to know how their data will be collected, stored, and used
3) Privacy - personal data must be securely stored and protected from unauthorized access
4) Intention - the intention behind collecting data must be considered to avoid potential harm
5) Outcomes - while intentions may be good, data analysis could inadvertently cause disparate impacts
Upholding data ethics helps businesses earn customer trust, which is essential to their success. Failure to do so can damage reputations and result
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4. So…what is social data intelligence?
Social Data Intelligence
Insight derived from social data
that organizations can use confidently,
at scale and in conjunction with other data sources
to make strategic decisions.
5. Challenges of integrating social data
Multiple internal
constituents and
interests
• Community managers &
customer service
• Marketing and digital
• Risk, compliance, legal, HR
• Market research
Requires new
analytical approaches
It’s big data!
• Variety
• Velocity
• Volume
Social Data (and
sometimes analysts)
lack enterprise
credibility
• Social data is new
• It lacks standards
• Analyst roles are new
6. Symantec harvests social data from across the web
and routes it to the central social business team,
who determines the business function
best equipped to serve the customer.
They classify Actionable Internet Mentions (AIMs) into 7
categories comprising different business functions:
1. Case
2. Query
3. Rant
4. Rave
5. Lead
6. RFE
7. Fraud
• Marketing
• Customer Support
• Engineering
• PR
• Product Management
• Legal
ECase Study: Symantec
7. Results across the enterprise
Customer Experience
Numerous support cases resolved
Converted many ‘ranters’ to ‘ravers’
Product Improvement
Rapidly identifies key areas
to prioritize R&D
Lead Generation & Nurturing
Generated hundreds of business
and consumer leads
Risk Mitigation
Uncovered hundreds of
fraudulent product pilots
9. Implications and trends
1. Flip your POV
Look at data from customer in, not silo out
2. Social data is “big data”
Embracing volumes, variety, and velocity
3. Big data disrupts organizations
The HiPPO phenomenon (data vs. intuition)
4. Faster all the time
Real, right-time enterprise.
10. Susan Etlinger
susan@altimetergroup.com
susanetlinger.com
Twitter: setlinger
THANK YOU
Disclaimer: Although the information and data used in this report have been produced and processed from sources
believed to be reliable, no warranty expressed or implied is made regarding the completeness, accuracy, adequacy or use
of the information. The authors and contributors of the information and data shall have no liability for errors or omissions
contained herein or for interpretations thereof. Reference herein to any specific product or vendor by trade name,
trademark or otherwise does not constitute or imply its endorsement, recommendation or favoring by the authors or
contributors and shall not be used for advertising or product endorsement purposes. The opinions expressed herein are
subject to change without notice.
Hinweis der Redaktion
The following case study illustrates how Symantec is addressing the challenges of meaningfully integrating social data with enterprise business processes and data. Note that, while many of the processes the company is using are still manual, the organization has established a clear vision for how it will solve both granular issues (integrating social signals into customer care) and more visionary ones, such as how it will use social data to inform a deeper and more strategic ongoing relationship with customers.Ultimately, the company vision is to integrate social data intelligence deeply into the organization to empower all 20,000 employees around the globe to engage with its 2.5 million customers.ObjectiveFounded in 1982, Symantec provides security, storage and systems management solutions to help customers secure and manage their information and identities independent of device. The company uses social data to optimize business value across the customer journey, as well as to drive revenue, improve efficiencies and mitigate risk. ApproachToday Symantec uses Salesforce Marketing Cloud to harvest social data — including posts, brand mentions, and comments — from across the web and sends it to a central team within the marketing organization that determines the business function best equipped to serve the customer. The central team, known as the Social Business Team, has established processes and workflows to route incoming queries and mentions to approximately 300 trained employees based on which product or issue is mentioned. Symantec has established specific tracks for specific products, but most notably classify what they call Actionable Internet Mentions (AIMs) into seven buckets, falling into different business functions and corresponding to various phases of the customer journey. The seven classifications are:1. Case: Request for help resolving real-time issue2. Query: Question that doesn’t require support resource3. Rant: Insult that merits brand management consideration4. Rave: Praise from Symantec brand advocate5. Lead: Pronouncement of near-term purchase decision6. RFE: Request to enhance a product with a new feature7. Fraud: Communication from an unauthorized provider of Symantec productsThese seven categories incorporate workflows for Symantec’s top 15 product lines and span business functions that include marketing, customer support, engineering, PR, product management, and legal. Following are some of the ways Symantec harvests social data:Customer Experience. Symantec aims to optimize customer experience at every touchpoint. For example, if a customer mentions the name of a product in a social post, that case is automatically assigned and routed to the appropriate support resource trained in social and on the specific product. Symantec also leverages existing content to streamline the process for both customer and employee, routing to support team members with a deep knowledge of product content who can answer the question or direct the customer to an existing thread in Symantec’s online community. For example, one of Symantec’s goals (and key metrics) is the conversion of “ranters” into “ravers.” The PR team is trained to surface a potential product issue hidden in the “rant,” re-tags the case accordingly, and conducts a “warm transfer”of the customer to the proper support staff to resolve the issue.13 “Raves” are also used to improve experience and are routed when appropriate to product marketing to say thank you, inquire about customer references, or invite the customer to become a blogger or forum advisor. For other customers, submitting ideas for innovation (or “Requests For Enhancements” — RFEs), Symantec routes suggestions to its product management team to help instruct development roadmap and priority• Lead Nurturing. Symantec also uses social data intelligence to generate and nurture leads, both for consumers and businesses. If the customer is comparing with a competitor, questioning renewal, asking for product specifications, or expressing frustration with a competitor’s product, listening tools enable Symantec to route these insights into its lead pipeline and engage in the most appropriate manner based on the customer’s comment. • Risk Mitigation. Symantec has also discovered a way to mitigate risk when analyzing data to build content for marketing. “The fraud protection value of social media monitoring came as a surprise to me: As we ran our product monitoring queries, we were alarmed to find a number of posts promoting and linking to illegal download sites,” explains Tristan Bishop, Symantec’s Director of Social Business. In addition to training its legal department to handle these posts, Symantec now actively monitors for fraud and helps preserve the integrity (and limit the potential for negative posts) around the product. ResultsSince rolling out this workflow, Symantec has resolved numerous support cases, converted many ranters into ravers, generated hundreds of business and consumer leads, rapidly identified key areas to prioritize for product development, and uncovered hundreds, if not thousands, of fraudulent product pilots. In the longer term, Bishop hopes to standardize all customer data within the same CRM system to provide full context for every employee. “When a Symantec employee interacts with a customer, we hope they’ll be able to view Symantec’s entire relationship with that customer: The customer’s sales history, their support history, and their social likes and shares of Symantec products and content. By giving our frontline staff this context, we can empower them to create a superior customer experience.”
The following case study illustrates how Symantec is addressing the challenges of meaningfully integrating social data with enterprise business processes and data. Note that, while many of the processes the company is using are still manual, the organization has established a clear vision for how it will solve both granular issues (integrating social signals into customer care) and more visionary ones, such as how it will use social data to inform a deeper and more strategic ongoing relationship with customers.Ultimately, the company vision is to integrate social data intelligence deeply into the organization to empower all 20,000 employees around the globe to engage with its 2.5 million customers.ObjectiveFounded in 1982, Symantec provides security, storage and systems management solutions to help customers secure and manage their information and identities independent of device. The company uses social data to optimize business value across the customer journey, as well as to drive revenue, improve efficiencies and mitigate risk. ApproachToday Symantec uses Salesforce Marketing Cloud to harvest social data — including posts, brand mentions, and comments — from across the web and sends it to a central team within the marketing organization that determines the business function best equipped to serve the customer. The central team, known as the Social Business Team, has established processes and workflows to route incoming queries and mentions to approximately 300 trained employees based on which product or issue is mentioned. Symantec has established specific tracks for specific products, but most notably classify what they call Actionable Internet Mentions (AIMs) into seven buckets, falling into different business functions and corresponding to various phases of the customer journey. The seven classifications are:1. Case: Request for help resolving real-time issue2. Query: Question that doesn’t require support resource3. Rant: Insult that merits brand management consideration4. Rave: Praise from Symantec brand advocate5. Lead: Pronouncement of near-term purchase decision6. RFE: Request to enhance a product with a new feature7. Fraud: Communication from an unauthorized provider of Symantec productsThese seven categories incorporate workflows for Symantec’s top 15 product lines and span business functions that include marketing, customer support, engineering, PR, product management, and legal. Following are some of the ways Symantec harvests social data:Customer Experience. Symantec aims to optimize customer experience at every touchpoint. For example, if a customer mentions the name of a product in a social post, that case is automatically assigned and routed to the appropriate support resource trained in social and on the specific product. Symantec also leverages existing content to streamline the process for both customer and employee, routing to support team members with a deep knowledge of product content who can answer the question or direct the customer to an existing thread in Symantec’s online community. For example, one of Symantec’s goals (and key metrics) is the conversion of “ranters” into “ravers.” The PR team is trained to surface a potential product issue hidden in the “rant,” re-tags the case accordingly, and conducts a “warm transfer”of the customer to the proper support staff to resolve the issue.13 “Raves” are also used to improve experience and are routed when appropriate to product marketing to say thank you, inquire about customer references, or invite the customer to become a blogger or forum advisor. For other customers, submitting ideas for innovation (or “Requests For Enhancements” — RFEs), Symantec routes suggestions to its product management team to help instruct development roadmap and priority• Lead Nurturing. Symantec also uses social data intelligence to generate and nurture leads, both for consumers and businesses. If the customer is comparing with a competitor, questioning renewal, asking for product specifications, or expressing frustration with a competitor’s product, listening tools enable Symantec to route these insights into its lead pipeline and engage in the most appropriate manner based on the customer’s comment. • Risk Mitigation. Symantec has also discovered a way to mitigate risk when analyzing data to build content for marketing. “The fraud protection value of social media monitoring came as a surprise to me: As we ran our product monitoring queries, we were alarmed to find a number of posts promoting and linking to illegal download sites,” explains Tristan Bishop, Symantec’s Director of Social Business. In addition to training its legal department to handle these posts, Symantec now actively monitors for fraud and helps preserve the integrity (and limit the potential for negative posts) around the product. ResultsSince rolling out this workflow, Symantec has resolved numerous support cases, converted many ranters into ravers, generated hundreds of business and consumer leads, rapidly identified key areas to prioritize for product development, and uncovered hundreds, if not thousands, of fraudulent product pilots. In the longer term, Bishop hopes to standardize all customer data within the same CRM system to provide full context for every employee. “When a Symantec employee interacts with a customer, we hope they’ll be able to view Symantec’s entire relationship with that customer: The customer’s sales history, their support history, and their social likes and shares of Symantec products and content. By giving our frontline staff this context, we can empower them to create a superior customer experience.”